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!19385 [MS][LITE] allow reduce op has only one input when axes param is not given 

Merge pull request !19385 from XianglongZeng/myms_new_3
This commit is contained in:
i-robot 2021-07-11 06:40:21 +00:00 committed by Gitee
parent 7ae900f699 9f0138b7de
commit 33a797daf9
6 changed files with 5 additions and 11 deletions
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8
mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/reduce_infer.c
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@ -52,7 +52,7 @@ int ReduceOnSelectedAxes(const TensorC *input, size_t num_axes, const int *actua
int ReduceInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **outputs, size_t outputs_size, int ReduceInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **outputs, size_t outputs_size,
OpParameter *parameter) { OpParameter *parameter) {
int check_ret = CheckAugmentNullSize(inputs, inputs_size, outputs, outputs_size, parameter, 2, 1); int check_ret = CheckAugmentNullSizeInputTwo(inputs, inputs_size, outputs, outputs_size, parameter, 1, 2, 1);
if (check_ret != NNACL_OK) { if (check_ret != NNACL_OK) {
return check_ret; return check_ret;
} }
@ -70,11 +70,11 @@ int ReduceInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC *
bool keep_dims = param->keep_dims_; bool keep_dims = param->keep_dims_;
int out_shape[MAX_SHAPE_SIZE] = {0}; int out_shape[MAX_SHAPE_SIZE] = {0};
const size_t out_shape_size = 0; const size_t out_shape_size = 0;
// get axes from input tensor if (inputs_size == 1) {
const TensorC *axes_input = inputs[1];
if (axes_input->shape_size_ == 1 && axes_input->shape_[0] == 0) {
return ReduceOnAllAxes(input, output, out_shape, out_shape_size, keep_dims); return ReduceOnAllAxes(input, output, out_shape, out_shape_size, keep_dims);
} }
// get axes from input tensor
const TensorC *axes_input = inputs[1];
int *axes = (int *)axes_input->data_; int *axes = (int *)axes_input->data_;
if (axes == NULL) { if (axes == NULL) {
return NNACL_NULL_PTR; return NNACL_NULL_PTR;

2
mindspore/lite/src/runtime/kernel/arm/fp16/reduce_fp16.cc
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@ -59,8 +59,6 @@ int ReduceFp16CPUKernel::Init() {
return ReSize(); return ReSize();
} }
int ReduceFp16CPUKernel::ReSize() { return ReduceBaseCPUKernel::ReSize(); }
int ReduceFp16CPUKernel::CallReduceUnit(int task_id) { int ReduceFp16CPUKernel::CallReduceUnit(int task_id) {
auto ret = auto ret =
reducer_(outer_size_, inner_size_, axis_size_, fp16_src_data_, fp16_dst_data_, task_id, op_parameter_->thread_num_); reducer_(outer_size_, inner_size_, axis_size_, fp16_src_data_, fp16_dst_data_, task_id, op_parameter_->thread_num_);

1
mindspore/lite/src/runtime/kernel/arm/fp16/reduce_fp16.h
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@ -36,7 +36,6 @@ class ReduceFp16CPUKernel : public ReduceBaseCPUKernel {
~ReduceFp16CPUKernel() = default; ~ReduceFp16CPUKernel() = default;
int Init() override; int Init() override;
int ReSize() override;
int Run() override; int Run() override;
int CallReduceUnit(int task_id); int CallReduceUnit(int task_id);

2
mindspore/lite/src/runtime/kernel/arm/fp32/reduce_fp32.cc
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@ -53,8 +53,6 @@ int ReduceCPUKernel::Init() {
return ReSize(); return ReSize();
} }
int ReduceCPUKernel::ReSize() { return ReduceBaseCPUKernel::ReSize(); }
int ReduceCPUKernel::CallReduceUnit(int task_id) { int ReduceCPUKernel::CallReduceUnit(int task_id) {
if (data_type_ == kDataTypeFloat) { if (data_type_ == kDataTypeFloat) {
if (!reducer_) { if (!reducer_) {

1
mindspore/lite/src/runtime/kernel/arm/fp32/reduce_fp32.h
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@ -54,7 +54,6 @@ class ReduceCPUKernel : public ReduceBaseCPUKernel {
} }
int Init() override; int Init() override;
int ReSize() override;
int Run() override; int Run() override;
int CallReduceUnit(int task_id); int CallReduceUnit(int task_id);

2
mindspore/lite/tools/converter/parser/onnx/onnx_reduce_parser.cc
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@ -34,11 +34,11 @@ ops::PrimitiveC *OnnxReduceParser::Parse(const onnx::GraphProto &onnx_graph, con
for (int i = 0; i < size; ++i) { for (int i = 0; i < size; ++i) {
axes.push_back(onnx_node_attr.ints(i)); axes.push_back(onnx_node_attr.ints(i));
} }
prim->AddAttr("axes", MakeValue(axes));
} else if (attribute_name == "keepdims") { } else if (attribute_name == "keepdims") {
prim->set_keep_dims(static_cast<bool>(onnx_node_attr.i())); prim->set_keep_dims(static_cast<bool>(onnx_node_attr.i()));
} }
} }
prim->AddAttr("axes", MakeValue(axes));
const auto &type = onnx_node.op_type(); const auto &type = onnx_node.op_type();
if (type == "ReduceMean") { if (type == "ReduceMean") {